Artificial Neural Networks in Manufacturing

2006 ◽  
pp. 236-261
Author(s):  
George A. Rovithakis ◽  
Stelios E. Perrakis ◽  
Manolis A. Christodoulou
Keyword(s):  
Neural Network ◽  
Job Shop ◽  
Industrial Test ◽  
Raw Material ◽  
System Stability ◽  
Test Case ◽  
Control Input ◽  
Processing Times ◽  
Network Controller ◽  
Average Lead

In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated by comparing its performance with conventional schedulers. Initially, after a brief reference to the context of existing solutions, the evaluated controller is thoroughly described. Namely, the employed dynamic neural network model, the subsequently derived continuous time neural network controller and the control input discretization that yield the actual dispatching times are presented. Next, the algorithm guaranteeing system stability and controller-signal boundedness and robustness are evaluated on an existing industrial test case that constitutes a highly nonacyclic deterministic job shop with extremely heterogeneous part-processing times. The major simulation study, employing the idealistic deterministic job-shop abstraction, provides extensive comparison with conventional schedulers, over a broad range of raw-material arrival rates, and through the extraction of several performance indices verifies its superb performance in terms of manufacturing-system stability and low makespan, low average lead times, WIP, inventory, and backlogging costs. Eventually, these extensive experiments highlight the practical value and the potential of the mathematical properties of the proposed neuroadaptive controller algorithm and its suitability for the control of nontrivial manufacturing cells.

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

Robust Scheduling for Flexible Job-Shop Problems with Uncertain Processing Times

2015 ◽  
Vol 135 (6) ◽  
pp. 713-720
Author(s):  
Wan-Ling Li ◽  
Tomohiro Murata ◽  
Muhammad Hafidz Fazli bin Md Fauadi
Keyword(s):  
Job Shop ◽  
Flexible Job Shop ◽  
Robust Scheduling ◽  
Processing Times

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

scholarly journals Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 942
Author(s):  
Myada Shadoul ◽  
Hassan Yousef ◽  
Rashid Al Abri ◽  
Amer Al-Hinai
Keyword(s):  
Adaptive Fuzzy Control ◽  
System Stability ◽  
Adaptive Fuzzy ◽  
Fuzzy Approximation ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Modelling Uncertainties ◽  
Grid Connected Inverter ◽  
Takagi Sugeno

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

Sign in / Sign up

Export Citation Format

Share Document